Health

Health blog translates biomedical research into practical steps for functional health and longevity. It's a scientist's view on how to use diet, exercise, lifestyle choices and technology to live healthier, longer lives.

A 95 years old psychology article holds the key to solving the obesity epidemic. It's not about a long forgotten medicine or an ancient psycho-trick. It's a simple observation about the dynamics of feeding. Vindicated by neurohormonal research, here is what it means to your struggle with extra pounds. [tweet this].

When Wallace Craig dissected the feeding behavior of doves, his experimental animal of choice, he discovered the existence of two distinct phases - an appetitive and a consummatory phase [1]. He defined appetite as "a state of agitation", which continues until food is presented, whereupon phase 2 begins. That's the phase you and I call eating. It's followed by a third phase of relative rest, which Craig called the state of satisfaction. You are forgiven if you now ask "what science nugget could possibly be hidden in this platitude". But the best-hidden gems are often those, which are in plain sight. In this case it's nothing less than the model explaining why so many of us wear dress sizes, ranging from "XL" to "Oh my God, look at this!", while none of us actually wants to be seen in them.

Before I get to the beauty of Craig's observation, let me also tell you what's the acid test for any biological model: it must make sense in evolutionary biology. If it does, it still may not be the final word, but if it doesn't we can safely discard it into the heap of wishful thinking. Keeping this in mind, let's get cracking.

When Craig published his paper in 1917 he described the behaviors of his doves as instinctive. In other words, being driven by some innate processes which require no conscious decision making nor any degree of intellect. Today we know a lot more about those "innate processes", particularly that they are the result of a complex conversation between neurons and hormones playing out in the recesses of the animal brain. Not only do we know the chains of command running from brain centre to periphery we also know the hormones (at least some of them) by names, such as Neuropeptide Y (NPY) or Leptin. You don't need to remember them. What you need to remember is that "instinctive" has matured from a black box stage to the stage of neurohormonal mechanisms, which can be tested quantitatively in the lab with experimental animals.

Evolutionary selection favored those who became fat easily. That's the essence of the "thrifty gene hypothesis". It's like Madonna. On the wrong side of 50, and ripe to be dethroned by something with greater sex appeal. In this case the contender's name is the "drifty gene hypothesis". Here is why you shouldn't be too dazzled about it. [tweet this].

Exactly 50 years ago, Neel suggested that the high rate of diabetes in our society is the result of evolutionary selection which favored those of our ancestors whose genes made them store fat more efficiently during periods of food abundance [1]. It's such a marvelously simple explanation that it doesn't take the brains of an Einstein to chatter about it at any dinner party where one wants to be remembered as quite the hobby geneticist. But to every party there is a party pooper. In this case two of them. John R. Speakman and Klaas R. Westerterp are telling us that the high prevalence rate of obesity and diabetes actually disproves the thrifty gene hypothesis [2].

In a nutshell their argument goes like this: our human and hominin ancestors have gone through so many feast and famine cycles over the past 2 million years, that, if it was for genetic selection, we should by now all be carriers of the genes that made caveman survive and modern man fat and diabetic. Since this is clearly not the case, the TGH can't be correct.

I'm a sucker for theories which challenge common wisdoms, so I enthusiastically read the authors' arguments. Now, let's see how this enthusiasm evaporated.

To a considerable extent, obesity is determined by genes. If you want to put a number on it, genetic factors explain about 60% of the variance in obesity metrics, such as the body mass index (BMI). That's the numbers we are getting from studies, which compare such metrics between identical twins and other sibling types [3]. Just as an aside: When you consider genes as the one condition which you can't change, 60% heritability still leaves a lot of wiggling room for you to fashion your own fate. That's good because obesity comes with a host of nasty diseases, none of which makes your life longer or more pleasant. Think diabetes. Of course, you know all that, and it is not really our subject here. We want to know why there is such a high prevalence of obesity prone people.

To answer this question Speakman and Westerterp compiled some insights from genetics and put them through a mathematical blender. That sounds far simpler than it really was. For that blender to give you an intelligent answer you need to feed it with intelligent data. Otherwise it's the old nonsense-in-nonsense-out" paradigm. In our case at hand there are three data segments which need to be considered.

First, there is biology: what happens to a human organism when it is exposed to fasting? How long will it survive?

Second, there is genetics: what do we know about those 60% of genetic causes? Are they concentrated in a handful of genes, or are they spread over hundreds? And what do we know about the mutation rates of genes? Obviously, the more causative genes, and the smaller the mutation rates the longer it will take for any genetic mutation (or allele) to become fixed in the genetic pool. "Fixed" being geneticist speak for "(almost) everybody has it".

Third, there is evolution & environment: how often did famines happen, and how many of our ancestors were affected by them at any one event?

Get the figures slightly wrong in any of those three segments and your result will be off track. And so will be your conclusions.

To get intelligent data, the two authors first went through an exemplary exercise of modeling what happens to a human organism when it is exposed to a zero-intake famine. That's not as straight forward as you might think, because our metabolism goes through at least three distinct phases when fasting in the extreme. These three phases are determined by our organism's way of storing energy reserves.

First, there is glucose, the building block of virtually all carbohydrates in our food. While our brain thrives almost exclusively on glucose, the body's glucose stores are remarkably small. Glucose is predominantly stored in the form of glycogen in muscle and liver tissue. It is these reserves which are tapped first, and they are typically depleted within 24 hours. If you are a marathon runner you do this depletion business a lot faster, say after 20 miles or so.

Since your brain still needs glucose, your body then starts to produce its own. Largely from lactate and glycerol, a component of fat. Which brings us to the second phase, where the body metabolizes its fat reserves. But even fat reserves don't last forever. Once they are depleted, the body begins to cannibalize its protein. Actually, weight loss in phase 2 is never a pure loss fat only. Proteins are being burnt at the same time but a at a lesser rate, until fat reserves have been depleted. And that's where fasting gets critical, because to your body, burning proteins for energy is like burning banknotes for warming your house: you go broke in no time. And "broke" means "dead" to your body.

Since time to death is a critical element in the mathematical model, the authors went through an exemplary effort of mapping the course from fully fed to fully dead. Interestingly, everybody reacts differently to this fasting business. Some people survive longer than others, even when they have the same BMI to start with. That's why Speakman and Westerterp applied three different models to predict survival time, all models representing those known different ways of adapting to starvation. For a severely obese 1.64 m tall female weighing 100 kg, the models predicted a survival time of 249-289 days. Imagine, that's about 8-9 months with no food at all.

Onto the genetics assumptions. The one thing we know for sure is that obesity is a multi-gene condition. Very multi-gene in fact, because genome-wide association studies (GWAS) have thrown up about 30 odd genes with a combined effect of explaining only 7% of those 60% of weight variance. So, we are assuming that the unexplained difference resides within another 200 or so genes, which we haven't even identified yet. Speakman translated this knowledge into an assumption of each individual gene having a net effect on fat storage of about 80g. That is, a carrier of a gene's "thrifty mutation" (or allele) would store 80g more fat than his peer with the "lean" version of the gene, with those 80g, translating into a 0.25% better chance of surviving a famine. With these assumptions the authors could then calculate how many famines it would take to weed out the unlucky ones whose "lean" genes didn't give them the 80g advantage. That calculation in itself is no rocket science. The authors took a given population size of 5 million people, exposed them to a virtual famine, after which the population had been appropriately decimated, and the percentage of "thrifty gene" carriers among the survivors had increased. They all mated happily after that until the population again reached 5 million. Then the next virtual famine struck, and so on.

How many famines would it take to eliminate the lean gene from the gene pool? Under the authors' assumptions about 6000 famine events.

They then made their final assumption: one famine happening every 150 years. That's 900000 years altogether for those 6000 famine events. Their conclusion: if the thrifty gene hypothesis and its assumption of selection pressure from catastrophic events was correct, we all should be obese today. Since we are not, the TGH is false.

The alternative explanation, which the authors offer is a "drifty gene hypothesis" as opposed to the thrifty version. "Drifty" referring to genetic drift, meaning that mutations of the genes, which regulate fat storage were never really subject to selection pressure, and what we see today is simply the result of a natural drift of genetic mutations over the eons of human existence.

The authors argue further that excessive fat storage was a distinct disadvantage for our earliest hominin ancestors, for reasons of predation. Think of it like that: while neither a fat man nor a lean man can outrun a saber toothed tiger, it's enough for the lean guy to run just a little faster than his fat bro'. Call it a stone-age version of the "first come, first serve" principle, at least from the tiger's perspective: the first man I get is the first man to serve me as breakfast.

The authors then suggest that once our ancestors discovered fire and spears and other things which placed them on top of the food chain, the selective pressure for the lean gene had vanished. Its thrifty sibling started to flourish, not because it was favored by famine-based selection pressure, but simply because man had taken tiger and co. out of the equation, and with it the selective pressure to NOT get fat. During those zillions of generations which separate the man-known-for-throwing-spears from the man-known-for-throwing-tantrum-when-the-iphone-doesn't-work, those 200 odd genes accumulated just enough mutations for many, but not all, of us to become obese and diabetic.

Up to this point one might buy into Speakman's and Westerterp's story. But here is the twist:

Speakman has written about the subject before. With a different tagline. In his 2006 paper he suggested that the selection pressure of famines in human history was too small to have caused the effects attributed to it by the thrifty gene hypothesis [4]. According to that paper, famines with severe mortality rates were rare and, most tellingly, a phenomenon of agricultural societies.

Indeed, the consensus view on famines in pre-agricultural vs. agricultural societies is that our hunter/gatherer ancestors were better fed and better protected against famines than their agricultural descendents. The hunter simply doesn't depend on a crop. Whereas when a crop fails, food shortage is inevitable for the agriculturalist. But even then, a true famine, where there is no food at all, typically requires a back-to-back failure of crops in consecutive years. And even then, as Speakman pointed out in his 2006 paper, mortality rates rarely exceeded 10% of the population, with those 10% coming almost exclusively from those who are either too young or too old to reproduce and thereby contribute to the gene pool after the famine is over. The author's message in 2006: Genetic mutations towards thrifty genes didn't have sufficient advantage or time to spread.

This little twist shows us that somebody is taking potshots at TGH:

Shot 1 (2006): Famines haven't been with us for long enough nor with sufficient severity to have exerted the selective pressure on which the thrifty gene hypothesis rests. Ergo, TGH is wrong.

Shot 2: Famines were so numerous and severe during human history that their combined selective pressure on the thrifty genes was sufficient to have made them a fixture in EVERYBODY'S genetic make-up. Since this is not the case, the TGH is wrong.

Science shouldn't be about taking potshots. Science is about the testing of falsifiable hypotheses in reproducible experiments. A mathematical model, such as the one presented in Speakman's most recent paper does not qualify as such.

Here is why: Given that mutations happen at the rate of 1.1 per 30-100 million base pairs, we all carry about 100 to 200 mutations in our DNA [5]. Not necessarily do those mutations affect actual genes coding for proteins. And if they do, most mutations confer a slight disadvantage, many have no effect on an organism's fitness, and only a few are favorable. Natural selection will weed out the deleterious ones, quickly fix the favorable ones and let the neutral ones accumulate at the given mutation rate. To complicate matters, all those processes happen at vastly different rates depending on the location on the DNA. That much we do know. What we don't know is how much these rates differ. We certainly can't know it for those genes, which we haven't even identified yet, as is the case for most of the hypothesized fat storage genes. That's why the mathematical model with which Speakman supports his argument against the validity of the thrifty gene hypothesis is in all likelihood not reflective of what has happened throughout evolution. Which means, it doesn't add any quantitative or objective evidence against the TGH.

In my next post I will tell you why I believe that the entire discussion misses the point. What we really want to know now is how to help people avoid becoming fat and diabetic in the first place. Decoding the genome and its evolutionary history doesn't do that trick. Because genes do not make us fat and diabetic, genes make proteins, nothing else. One part of those proteins are the hormones. They drive our moods and emotions, our likes and our dislikes and, believe it or not, all our behaviors, from feeding to physical activity. For those latter two I have suggested an explanatory model in my dissertation thesis.

This model tries not only to explain why we eat too much and move too little, despite having the best intentions to do otherwise, and while being aware of all the life threatening consequences. But, more importantly, without having to have a complete understanding of all those hormonal happenings, the model suggests a practical and testable solution to oppose those genetically encoded mechanisms for a longer and healthier life. Think of your car: You don't need to understand the mechanism of its gearbox to operate it for an optimal ride.

Achieving the same thing with your life could turn out to be a gratifying pastime while my geneticist colleagues work on unraveling the enigma of the genetics of obesity. Whatever newer and sexier model they develop to explain the genetic origins of obesity, we might look at it like we look at Madonna and her variants: offering lots of entertainment value, but little of practical use. [tweet this].

2.Speakman JR, Westerterp KR: A mathematical model of weight loss under total starvation and implications of the genetic architecture of the modern obesity epidemic for the thrifty-gene hypothesis. Disease models & mechanisms 2012.

4.Speakman JR: Thrifty genes for obesity and the metabolic syndrome--time to call off the search?Diabetes & vascular disease research : official journal of the International Society of Diabetes and Vascular Disease 2006, 3(1):7-11.

Speakman JR, & Westerterp KR (2012). A mathematical model of weight loss under total starvation and implications of the genetic architecture of the modern obesity epidemic for the thrifty-gene hypothesis. Disease models & mechanisms PMID: 22864023

Speakman JR (2006). Thrifty genes for obesity and the metabolic syndrome--time to call off the search? Diabetes & vascular disease research : official journal of the International Society of Diabetes and Vascular Disease, 3 (1), 7-11 PMID: 16784175

Public health has been telling you for years: you are fat because you move too little and eat too much. And yes, it's your fault if you don't break a sweat every day to keep your waist line in check. But research says, that's not the entire truth. In fact, public health might have taken the easy way out, and here is how it could finally make amends. [tweet this].

If an alien scientist came to earth to study us in the same way in which we study lab rats, he would come to the same simple conclusion as we do: give those animals more than enough food, take away the need to move around, and what you'll get is a population of mostly overweight individuals. I say "mostly" because there are always the odd ones who fall away from the norm. What fascinates me most in this image is the fact that, while mice and rats probably do not communicate among each other the benefits of staying slim, we humans do so and still, the result is the same. What our alien researcher sees is biology trumping consciousness. For a good reason. Neither rats nor humans would survive in their natural habitat without the ability to store excess calories as fat, which then sees them through the inevitable lean periods. It gave our ancestors a good shot at survival, with no or little chance to become overweight. At least not then.

Today, obesity is the new normal. I won't bore you with the percentages. You hear and read about them in the media almost daily, with one or the other pundit citing the ever increasing number of people who are overweight or outright fat (the politically correct term being "obese"). Not that any of those pundits offers any solution or view of things other than that too little exercise and too much food are the cause. Those platitudes are typically topped off with denouncing people's weakness to do something about it, such as exercising more and eating less. When you look at the effectiveness of public health calls for exercising more and eating less, you'll find that overweight and obesity have increased nicely alongside those calls. Which simply means one thing: we need to do something differently.

Now, remember, I said there are always some odd individuals who seem to escape the fate of the majority of our experimental animals, be that rats in the lab or humans in free living conditions. It is here where we ought to look at what makes them so different. And whether this difference is in their genetic program or in their mental ability to override this program.

The funny thing is, the answer to this question has been relatively clear for years, but hardly anybody seems to draw the right conclusions from it. Just about a week ago, another wonderful study has emerged on this subject.

Britt Eriksson and her colleagues investigated the correlation between body composition development and energy expenditure through physical activity in 1.5 year old infants [1]. That's not a first, but the way they did it is. When you look at energy expenditure of any individual it is necessary to know how much of this energy expenditure comes from basal metabolic rate (BMR). This BMR tells us how much energy an organism needs to maintain life under resting conditions. There are large differences in these rates between individuals, such that two persons who share the same body weight, height and composition and who do the same type of exercise may burn substantially different amounts of calories, simply because one person has a higher basal metabolic rate than the other. So, If you want to know exactly how much of an individual's total energy expenditure is coming from physical activity, you better have accurate knowledge about his basal metabolic rate because you need to subtract it from total energy he or she burns. In previous studies of infants, physical activity levels (PAL) had been estimated based on predicted BMR rather than on actually measured BMR. Obviously, if your BMR prediction is incorrect so will be your conclusions about PAL. That's why Eriksson and her colleagues objectively measured basal metabolic rates. They did so by analyzing carbon dioxide production and oxygen consumption while infants slept under a ventilated hood system. Add to this the researchers' way of measuring total energy expenditure with the gold standard doubly labeled water method, and what you get is the most accurate differentiation between BMR and PAL possible in living humans.

Our researchers did all those measurements on 44 children aged 1.5 years. All of them had participated in a body composition study when they were 1 and 12 weeks old. Body composition was again measured in the current study. Before we look at the correlation between body fatness and PAL in those 1.5 year old children, let's recall what is normal in human development during infancy.

Healthy infants typically gain body fat, expressed as a percentage of bodyweight, during the first 6 months of life, after which the total body fat percentage (TBF%) slowly decreases. By the way, that was the case in only about 20% of the infants in this study. The majority increased their body fat percentage but with large differences between individuals. At age 1.5 years TBF% varied between 21% and 35%. And these changes in body fat correlated with the physical activity levels of the infants, such that those with a higher PAL had decreased their body fat percentage more than those with a lower PAL. The beauty of investigating these associations in infants is that you don't need to worry about your study subjects' volitional exercise habits, such as treadmill running, mountain biking or kicking ass instead of writing anonymous comments to blog posts. All their physical activity is non-exercise activity. I'll get to this important distinction in a moment. The point here is: genetic influences show up relatively unmasked. If there are such large inter-individual differences in body fat development already being evident in the earliest years of life, we have every reason to assume that there is a phenotype and a genotype which is better protected against fat gain than others. We also know that body fat percentage in the youngest years tracks into adolescence and on into adulthood.

Which of course also means that we should see such differences in adults, too. In fact we have been seeing them for more than 10 years, but somehow these observations don't make it into the media where the doom and gloom prophets of obesity have our ears and eyes but no solutions to offer.

Back to those studies: Levine and colleagues put 16 non-obese young adults, aged 25-36, on an 8-weeks supervised diet which provided a daily excess of 1000 Kcal over what each individual needed for weight maintenance [2]. The participants had to maintain their usual level of exercise throughout the experiment. Physical activity and body composition were measured with the same gold standard methods, which the Eriksson group used on their infants. As a group, the participants of the overfeeding experiment stored 44% of the excess kcal as fat, and dissipated 53% through increased energy expenditure.

But those average values over a group of people don't interest us here. What we want to know is how much difference was there between participants. Well, fat gain varied more than 10-fold from a minimal increase of 360 Grams to a whopping 4.23 kg. Think about this for a moment: you let 16 people gorge themselves on a daily excess of 1000 kcal for 8 weeks and what you get is one whose weight remains virtually the same, while another gains more than 9 pounds, and all the other 14 show up anywhere in between those two.

The laws of physics tell us that energy cannot be lost or created, it can only be converted from one form to another. What this means to our weight gain experiment is that those who didn't store the energy as fat must have burned it somehow through physical activity. But how could that have happened if all participants kept their exercise on an even keel throughout the experiment? Had an enormous increase of BMR protected them against weight gain? Our researchers didn't think so, because experiments on BMR response to over- and underfeeding have been fairly consistent, showing only small changes in the range of 5%. Levine's participants were no exception to that rule. So, what happened?

The answer is in the details of what constitutes physical activity. There are two components, one of which you certainly know: exercise. Then there is the other, which I just mentioned a few lines earlier. It's called NEAT, which is short for non-exercises activity thermogenesis. In a less convoluted way it means the energy you burn through acitivities of daily living, fidgeting, spontaneous muscle contractions and maintaining or adjusting posture while not lying down. In other words, the energy you burn through physical activity which is not volitional exercise.

NEAT accounted for over 70% of the increase in daily energy expenditure, with an average increase of 336kcal/day. Mind you, this was the average over the entire group. Far more interesting, again, is the range, which spanned from a decrease of 98 kcal/day to an increase of 692 kcal/day. It's the same picture we saw in the fat weight development. And yes, the larger a participant's increase in NEAT the smaller his weight gain. The fellow with the 692 kcal/ increase subconsciously moved around more often. He had increased his strolling-equivalent activity by an average of 15 minutes per waking hour! Interestingly, the 4 female participants in this study had the smallest changes in NEAT. While this study is certainly underpowered to tell us anything about gender differences, its observations fits neatly with an another observation: The age-dependent increase of obesity risk is steeper for women than for men.

Now, back to the study results. If NEAT is NON-VOLUNTARY activity energy expenditure, then conscious rationally driven behavior has nothing to do with it. It's purely physiology talking. It's our genes' handwriting. And if this handwriting reveals such a substantial effect on weight development, shouldn't we look at means to increase NEAT, rather than keeping our current tunnel vision on exercise, which we already know is so difficult to adopt for most people? Let's put some effort into designing "obligatory" NEAT into our life. Or rather, designing NEAT killers (such as remote controls) out of it.

To our alien researcher, this might just be the next experiment, as it is for his human peers who are already experimenting with running wheels and wheel locks in their lab rats' cages. After all, a 332 kcal/day deficit translates into almost 14 kg of fat over a year. That's certainly something which public health ought to be interested in.

1.Eriksson B, Henriksson H, Löf M, Hannestad U, Forsum E: Body-composition development during early childhood and energy expenditure in response to physical activity in 1.5-y-old children. The American Journal of Clinical Nutrition 2012.